Rethinking Performance Measures of RNA Secondary Structure Problems

• URL: http://arxiv.org/abs/2401.05351v1
• Date: Mon, 4 Dec 2023 08:46:24 GMT
• Title: Rethinking Performance Measures of RNA Secondary Structure Problems
• Authors: Frederic Runge, J\"org K. H. Franke, Daniel Fertmann, Frank Hutter
• Abstract summary: Deep learning methods have surpassed traditional algorithms by predicting complex features like pseudoknots and multi-interacting base pairs. We propose the Weisfeiler-Lehman graph kernel (WL) as an alternative metric.
• Score: 42.25267871026153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate RNA secondary structure prediction is vital for understanding cellular regulation and disease mechanisms. Deep learning (DL) methods have surpassed traditional algorithms by predicting complex features like pseudoknots and multi-interacting base pairs. However, traditional distance measures can hardly deal with such tertiary interactions and the currently used evaluation measures (F1 score, MCC) have limitations. We propose the Weisfeiler-Lehman graph kernel (WL) as an alternative metric. Embracing graph-based metrics like WL enables fair and accurate evaluation of RNA structure prediction algorithms. Further, WL provides informative guidance, as demonstrated in an RNA design experiment.

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